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Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence

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  • Joshua C C Chan
  • Cody Y L Hsiao

Abstract

Financial time series often exhibit properties that depart from the usual assumptions of serial independence and normality. These include volatility clustering, heavy-tailedness and serial dependence. A voluminous literature on different approaches for modeling these empirical regularities has emerged in the last decade. In this paper we review the estimation of a variety of highly flexible stochastic volatility models, and introduce some efficient algorithms based on recent advances in state space simulation techniques. These estimation methods are illustrated via empirical examples involving precious metal and foreign exchange returns. The corresponding Matlab code is also provided.

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Bibliographic Info

Paper provided by Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University in its series CAMA Working Papers with number 2013-74.

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Length: 23 pages
Date of creation: Nov 2013
Date of revision:
Handle: RePEc:een:camaaa:2013-74

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Keywords: stochastic volatility; scale mixture of normal; state space model; Markov chain Monte Carlo; financial data;

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References

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  1. Joshua C.C. Chan & Garry Koop & Roberto Leon Gonzales & Rodney W. Strachan, 2010. "Time Varying Dimension Models," ANU Working Papers in Economics and Econometrics, Australian National University, College of Business and Economics, School of Economics 2010-523, Australian National University, College of Business and Economics, School of Economics.
  2. Wang, Joanna J.J. & Chan, Jennifer S.K. & Choy, S.T. Boris, 2011. "Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(1), pages 852-862, January.
  3. Sangjoon Kim, Neil Shephard & Siddhartha Chib, . "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers, Economics Group, Nuffield College, University of Oxford W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
  4. Joshua C C Chan, 2012. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," ANU Working Papers in Economics and Econometrics, Australian National University, College of Business and Economics, School of Economics 2012-591, Australian National University, College of Business and Economics, School of Economics.
  5. Ivan Jeliazkov & Rui Liu, 2010. "A model-based ranking of U.S. recessions," Economics Bulletin, AccessEcon, vol. 30(3), pages 2289-2296.
  6. Koop, Gary M & Poirier, Dale J & Tobias, Justin, 2007. "Bayesian Econometric Methods," Staff General Research Papers, Iowa State University, Department of Economics 12722, Iowa State University, Department of Economics.
  7. McCausland, William J. & Miller, Shirley & Pelletier, Denis, 2011. "Simulation smoothing for state-space models: A computational efficiency analysis," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(1), pages 199-212, January.
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Cited by:
  1. Nonejad, Nima, 2014. "Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks," MPRA Paper 55664, University Library of Munich, Germany.

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